Python GEE to extract featurecollection timeseries from Landsat imageries

Python GEE to extract featurecollection timeseries from Landsat imageries

By : user3100216
Date : January 12 2021, 01:40 AM
this one helps. I have a featurecollection for which I need to extract timeseries of NDWI from Landsat imageries. , Try And instead of and:
code :

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Python: script to import multiple LANDSAT images to Grass GIS

Python: script to import multiple LANDSAT images to Grass GIS

By : Thevoidrzx
Date : March 29 2020, 07:55 AM
hope this fix your issue I believe your main problem is that dirname in os.walk() returns a list (not a string), so your subsequent strings (namely dirname = os.path.join(dirpath,dirname)) are a bit malformed. Here is one possible alternative - to test this, I used the full path to the directory as sys.argv[1], but you can make it more dynamic to suit your case. Also, avoid using variable names such as file since they are Python keywords. I couldn't test out your grass.* functions, but hopefully this will be a clear enough example so you can tweak how you need. os.walk() natively handles a lot of standard parsing, so you can remove some of the directory-manipulating functions:
code :
def import_tifs(dirpath):
  for dirpath, dirname, filenames in os.walk(dirpath):
    # Iterate through the files in the current dir returned by walk()
    for tif_file in filenames:
      # If the suffix is '.TIF', process
      if tif_file.upper().endswith('.tif'):
        # This will contain the full path to your file
        full_path = os.path.join(dirpath, tif_file)

        # tif_file will already contain the name, so you can call from here
        grass.message('Importing %s -> %s@%s...' % (full_path, tif_file, dirpath))

                          flags = 'o',
                          input = full_path,
                          output = tif_file,
                          quiet = True,
                          overwrite = True)
Python script to read LANDSAT XML metafile

Python script to read LANDSAT XML metafile

By : Mike Skutta
Date : March 29 2020, 07:55 AM
I wish this helpful for you I need to obtain the scene ID for several Landsat images. The metadata for the images is contained in an xml file: , Try this one:
code :

from xml.dom.minidom import parse

dom = parse('C:/Users/Simon/Documents/tif/Metadata/metadata_test1.xml')

ids  = dom.getElementsByTagName('sceneID')
for id in ids:
    print "%s" % id.childNodes[0].nodeValue
Python xarray: Extract first and last time value within each month of a timeseries

Python xarray: Extract first and last time value within each month of a timeseries

By : cycneavox
Date : March 29 2020, 07:55 AM
wish of those help Since 'time' is a coordinate in the DataArray you provided, for the moment it is not possible1 preform resample directly upon it. A possible workaround is to create a new DataArray with the time coordinate values as a variable (still linked with the same coordinate 'time')
If arr is the DataArray you are starting from I would suggest something like this:
code :
time = xray.DataArray(arr.time.values, coords=[arr.time.values], dims=['time'])
time_first = time.resample('1M', 'time', how='first')
time_last = time.resample('1M', 'time', how='last')
time_diff = time_last - time_first
Google Earth Engine: how to map a function over a collection of all Landsat sensors to create NDVI timeseries

Google Earth Engine: how to map a function over a collection of all Landsat sensors to create NDVI timeseries

By : Avelar
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The system:time_start property was lost at the masking stage. This property is necessary for plotting time series, as it is used to construct the time axis. Change those two functions to reassign that property to the image the functions return, as in:
code :
var maskL8SR = function(image) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = ee.Number(2).pow(3).int();
  var cloudsBitMask = ee.Number(2).pow(5).int();
  // Get the QA band.
  var qa = image.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
 var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
 return image
      // Scale the data to reflectance and temperature.
      .select(['B4', 'B5'], ['Red', 'NIR']).multiply(0.0001)
      .set('system:time_start', image.get('system:time_start'));
Extract all noon data from a timeseries

Extract all noon data from a timeseries

By : Hsu Ming-Yi
Date : March 29 2020, 07:55 AM
wish helps you I am working on hourly electric load data which has 8760 points for the entire year It looks like this , Setup
code :
dr = pd.date_range(start='2018-01-01', periods=48, freq='1H')
df = pd.DataFrame(dict(Date=dr))
12 2018-01-01 12:00:00
36 2018-01-02 12:00:00
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